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Vision-language pre-training (VLP) models exhibit remarkable capabilities in comprehending both images and text, yet they remain susceptible to multimodal adversarial examples (AEs).
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Huang, Y., Juefei-Xu, F., Guo, Q., Zhang, J., Wu, Y., Hu, M., Li, T., Pu, G., Liu, Y.: Personalization as a shortcut for few-shot backdoor attack against text-to-image diffusion models. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 38, pp. 21169–21178 (2024)
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Park, J., Miller, P., McLaughlin, N.: Hard-label based small query black-box adversarial attack. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 3986–3995 (2024)
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